{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PyCaret 2 Anomaly Example\n",
    "This notebook is created using PyCaret 2.0. Last updated : 31-07-2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# check version\n",
    "from pycaret.utils import version\n",
    "version()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Loading Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  \n",
       "0  0.608234  0.723782  0.733591  \n",
       "1  0.331679  0.429297  0.367422  \n",
       "2  0.861309  0.899017  0.088600  \n",
       "3  0.158963  0.073715  0.208463  \n",
       "4  0.558449  0.885169  0.182754  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "data = get_data('anomaly')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_37ac72ba_d32c_11ea_9254_5076af549583\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row0_col0\" class=\"data row0 col0\" >session_id </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row1_col0\" class=\"data row1 col0\" >Original Data </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row1_col1\" class=\"data row1 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row2_col0\" class=\"data row2 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row3_col0\" class=\"data row3 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row3_col1\" class=\"data row3 col1\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row4_col0\" class=\"data row4 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row4_col1\" class=\"data row4 col1\" >0</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row5_col0\" class=\"data row5 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row5_col1\" class=\"data row5 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row6_col0\" class=\"data row6 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row7_col0\" class=\"data row7 col0\" >Transformed Data </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row7_col1\" class=\"data row7 col1\" >(1000, 10)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row8_col0\" class=\"data row8 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row8_col1\" class=\"data row8 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row9_col0\" class=\"data row9 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row9_col1\" class=\"data row9 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row10_col0\" class=\"data row10 col0\" >Normalize </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row11_col0\" class=\"data row11 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row11_col1\" class=\"data row11 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row12_col0\" class=\"data row12 col0\" >Transformation </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row13_col0\" class=\"data row13 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row13_col1\" class=\"data row13 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row14_col0\" class=\"data row14 col0\" >PCA </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row15_col0\" class=\"data row15 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row15_col1\" class=\"data row15 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row16_col0\" class=\"data row16 col0\" >PCA components </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row17_col0\" class=\"data row17 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row18_col0\" class=\"data row18 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row19_col0\" class=\"data row19 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row19_col1\" class=\"data row19 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row20_col0\" class=\"data row20 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row20_col1\" class=\"data row20 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row21_col0\" class=\"data row21 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row21_col1\" class=\"data row21 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row22_col0\" class=\"data row22 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row22_col1\" class=\"data row22 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_37ac72ba_d32c_11ea_9254_5076af549583level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row23_col0\" class=\"data row23 col0\" >Group Features </td>\n",
       "                        <td id=\"T_37ac72ba_d32c_11ea_9254_5076af549583row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22973a19bc8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020/07/31 08:49:05 WARNING mlflow.tracking.context.git_context: Failed to import Git (the Git executable is probably not on your PATH), so Git SHA is not available. Error: No module named 'repository'\n"
     ]
    }
   ],
   "source": [
    "from pycaret.anomaly import *\n",
    "ano1 = setup(data, session_id=123, log_experiment=True, experiment_name='anomaly1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>abod</th>\n",
       "      <td>Angle-base Outlier Detection</td>\n",
       "      <td>pyod.models.abod.ABOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iforest</th>\n",
       "      <td>Isolation Forest</td>\n",
       "      <td>pyod.models.iforest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <td>Clustering-Based Local Outlier</td>\n",
       "      <td>pyod.models.cblof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cof</th>\n",
       "      <td>Connectivity-Based Outlier Factor</td>\n",
       "      <td>pyod.models.cof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>histogram</th>\n",
       "      <td>Histogram-based Outlier Detection</td>\n",
       "      <td>pyod.models.hbos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>k-Nearest Neighbors Detector</td>\n",
       "      <td>pyod.models.knn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lof</th>\n",
       "      <td>Local Outlier Factor</td>\n",
       "      <td>pyod.models.lof</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>svm</th>\n",
       "      <td>One-class SVM detector</td>\n",
       "      <td>pyod.models.ocsvm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pca</th>\n",
       "      <td>Principal Component Analysis</td>\n",
       "      <td>pyod.models.pca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcd</th>\n",
       "      <td>Minimum Covariance Determinant</td>\n",
       "      <td>pyod.models.mcd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sod</th>\n",
       "      <td>Subspace Outlier Detection</td>\n",
       "      <td>pyod.models.sod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sos</th>\n",
       "      <td>Stochastic Outlier Selection</td>\n",
       "      <td>pyod.models.sos</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Name              Reference\n",
       "ID                                                                 \n",
       "abod            Angle-base Outlier Detection  pyod.models.abod.ABOD\n",
       "iforest                     Isolation Forest    pyod.models.iforest\n",
       "cluster       Clustering-Based Local Outlier      pyod.models.cblof\n",
       "cof        Connectivity-Based Outlier Factor        pyod.models.cof\n",
       "histogram  Histogram-based Outlier Detection       pyod.models.hbos\n",
       "knn             k-Nearest Neighbors Detector        pyod.models.knn\n",
       "lof                     Local Outlier Factor        pyod.models.lof\n",
       "svm                   One-class SVM detector      pyod.models.ocsvm\n",
       "pca             Principal Component Analysis        pyod.models.pca\n",
       "mcd           Minimum Covariance Determinant        pyod.models.mcd\n",
       "sod               Subspace Outlier Detection        pyod.models.sod\n",
       "sos             Stochastic Outlier Selection        pyod.models.sos"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iforest = create_model('iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = create_model('knn', fraction = 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Assign Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Label     Score  \n",
       "0  0.608234  0.723782  0.733591      0 -0.035865  \n",
       "1  0.331679  0.429297  0.367422      0 -0.084927  \n",
       "2  0.861309  0.899017  0.088600      1  0.025356  \n",
       "3  0.158963  0.073715  0.208463      1  0.042415  \n",
       "4  0.558449  0.885169  0.182754      0 -0.023408  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iforest_results = assign_model(iforest)\n",
    "iforest_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Analyze Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('67e4907b-0b4c-469f-9246-ce1f14567f1c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(iforest, plot = 'umap')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Predict Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "      <th>Label</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.035865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.084927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.025356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "      <td>1</td>\n",
       "      <td>0.042415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.023408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  Label     Score  \n",
       "0  0.608234  0.723782  0.733591      0 -0.035865  \n",
       "1  0.331679  0.429297  0.367422      0 -0.084927  \n",
       "2  0.861309  0.899017  0.088600      1  0.025356  \n",
       "3  0.158963  0.073715  0.208463      1  0.042415  \n",
       "4  0.558449  0.885169  0.182754      0 -0.023408  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_new = predict_model(iforest, data=data)\n",
    "pred_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Save / Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(iforest, model_name='iforest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n",
      "[Pipeline(memory=None,\n",
      "         steps=[('dtypes',\n",
      "                 DataTypes_Auto_infer(categorical_features=[],\n",
      "                                      display_types=True, features_todrop=[],\n",
      "                                      ml_usecase='regression',\n",
      "                                      numerical_features=[],\n",
      "                                      target='dummy_target',\n",
      "                                      time_features=[])),\n",
      "                ('imputer',\n",
      "                 Simple_Imputer(categorical_strategy='not_available',\n",
      "                                numeric_strategy='mean',\n",
      "                                target_variable=None)),\n",
      "                ('new_levels1',\n",
      "                 New_Catagorical_L...\n",
      "                                                    target='dummy_target')),\n",
      "                ('feature_time',\n",
      "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
      "                ('group', Empty()), ('scaling', Empty()),\n",
      "                ('P_transform', Empty()), ('binn', Empty()),\n",
      "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
      "                ('dummy', Dummify(target='dummy_target')),\n",
      "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
      "                ('pca', Empty())],\n",
      "         verbose=False), IForest(behaviour='new', bootstrap=False, contamination=0.05,\n",
      "    max_features=1.0, max_samples='auto', n_estimators=100, n_jobs=1,\n",
      "    random_state=123, verbose=0)]\n"
     ]
    }
   ],
   "source": [
    "loaded_iforest = load_model('iforest')\n",
    "print(loaded_iforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"094b4c73-e34a-42f0-af06-3cb0a0f293d6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"094b4c73-e34a-42f0-af06-3cb0a0f293d6\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"cb66d144-4568-4e6f-ac78-2db3b4993802\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"cb66d144-4568-4e6f-ac78-2db3b4993802\">DataTypes_Auto_infer</label><div class=\"sk-toggleable__content\"><pre>DataTypes_Auto_infer(ml_usecase='regression', target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f6a278b6-a5f0-4938-a0c9-3258a90a77ce\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f6a278b6-a5f0-4938-a0c9-3258a90a77ce\">Simple_Imputer</label><div class=\"sk-toggleable__content\"><pre>Simple_Imputer(categorical_strategy='not_available', numeric_strategy='mean',\n",
       "               target_variable=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"093cad93-1c05-4fc8-a5eb-b773df998402\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"093cad93-1c05-4fc8-a5eb-b773df998402\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"92f67d76-bc38-4cf5-a35a-650df307b980\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"92f67d76-bc38-4cf5-a35a-650df307b980\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f343c346-95b3-478e-869b-a5a737764859\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f343c346-95b3-478e-869b-a5a737764859\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b2578b44-ad01-46ae-82db-00d8a9d040c0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b2578b44-ad01-46ae-82db-00d8a9d040c0\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9cbacab7-32bd-4aab-b6a8-ef476ed9337e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9cbacab7-32bd-4aab-b6a8-ef476ed9337e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"add2e689-f380-48e1-8147-12dda9744738\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"add2e689-f380-48e1-8147-12dda9744738\">New_Catagorical_Levels_in_TestData</label><div class=\"sk-toggleable__content\"><pre>New_Catagorical_Levels_in_TestData(replacement_strategy='least frequent',\n",
       "                                   target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b5baff3d-09ab-4af7-92b3-ba534a1d39d6\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b5baff3d-09ab-4af7-92b3-ba534a1d39d6\">Make_Time_Features</label><div class=\"sk-toggleable__content\"><pre>Make_Time_Features(list_of_features=None)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"34f3c33b-8f62-446d-9d1d-2d4912d49bdf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"34f3c33b-8f62-446d-9d1d-2d4912d49bdf\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"93b68bb6-2322-4b42-9bba-5d91ed2aeac7\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"93b68bb6-2322-4b42-9bba-5d91ed2aeac7\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b0979e18-1315-4709-9ae9-bdd3e49613a5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b0979e18-1315-4709-9ae9-bdd3e49613a5\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e3794ee1-0698-4e25-a211-7faeded9c30e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e3794ee1-0698-4e25-a211-7faeded9c30e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"92deacf1-2fac-46ca-be62-7aa291acc74e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"92deacf1-2fac-46ca-be62-7aa291acc74e\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5f6385bb-537f-4bbf-aa9b-429e3796fbbd\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5f6385bb-537f-4bbf-aa9b-429e3796fbbd\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"06579249-8b38-4e36-aae9-9b42ef1e437a\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"06579249-8b38-4e36-aae9-9b42ef1e437a\">Dummify</label><div class=\"sk-toggleable__content\"><pre>Dummify(target='dummy_target')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c277bb0a-7349-402c-bbd3-c7ed9e5b054b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c277bb0a-7349-402c-bbd3-c7ed9e5b054b\">Clean_Colum_Names</label><div class=\"sk-toggleable__content\"><pre>Clean_Colum_Names()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"084f3f5c-e282-4f32-b2a4-687d2e3ff357\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"084f3f5c-e282-4f32-b2a4-687d2e3ff357\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d7116d59-ac58-4d24-b4cc-703ccb556607\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d7116d59-ac58-4d24-b4cc-703ccb556607\">Empty</label><div class=\"sk-toggleable__content\"><pre>Empty()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('dtypes',\n",
       "                 DataTypes_Auto_infer(categorical_features=[],\n",
       "                                      display_types=True, features_todrop=[],\n",
       "                                      ml_usecase='regression',\n",
       "                                      numerical_features=[],\n",
       "                                      target='dummy_target',\n",
       "                                      time_features=[])),\n",
       "                ('imputer',\n",
       "                 Simple_Imputer(categorical_strategy='not_available',\n",
       "                                numeric_strategy='mean',\n",
       "                                target_variable=None)),\n",
       "                ('new_levels1',\n",
       "                 New_Catagorical_L...\n",
       "                                                    target='dummy_target')),\n",
       "                ('feature_time',\n",
       "                 Make_Time_Features(list_of_features=None, time_feature=[])),\n",
       "                ('group', Empty()), ('scaling', Empty()),\n",
       "                ('P_transform', Empty()), ('binn', Empty()),\n",
       "                ('fix_perfect', Empty()), ('rem_outliers', Empty()),\n",
       "                ('dummy', Dummify(target='dummy_target')),\n",
       "                ('clean_names', Clean_Colum_Names()), ('fix_multi', Empty()),\n",
       "                ('pca', Empty())],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='diagram')\n",
    "loaded_iforest[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import set_config\n",
    "set_config(display='text')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Deploy Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Succesfully Deployed on AWS S3\n"
     ]
    }
   ],
   "source": [
    "deploy_model(iforest, model_name = 'iforest-aws', authentication = {'bucket' : 'pycaret-test'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Get Config / Set Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "      <th>Col7</th>\n",
       "      <th>Col8</th>\n",
       "      <th>Col9</th>\n",
       "      <th>Col10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.263995</td>\n",
       "      <td>0.764929</td>\n",
       "      <td>0.138424</td>\n",
       "      <td>0.935242</td>\n",
       "      <td>0.605867</td>\n",
       "      <td>0.518790</td>\n",
       "      <td>0.912225</td>\n",
       "      <td>0.608234</td>\n",
       "      <td>0.723782</td>\n",
       "      <td>0.733591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.546092</td>\n",
       "      <td>0.653975</td>\n",
       "      <td>0.065575</td>\n",
       "      <td>0.227772</td>\n",
       "      <td>0.845269</td>\n",
       "      <td>0.837066</td>\n",
       "      <td>0.272379</td>\n",
       "      <td>0.331679</td>\n",
       "      <td>0.429297</td>\n",
       "      <td>0.367422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.336714</td>\n",
       "      <td>0.538842</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>0.074515</td>\n",
       "      <td>0.332993</td>\n",
       "      <td>0.365792</td>\n",
       "      <td>0.861309</td>\n",
       "      <td>0.899017</td>\n",
       "      <td>0.088600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.092108</td>\n",
       "      <td>0.995017</td>\n",
       "      <td>0.014465</td>\n",
       "      <td>0.176371</td>\n",
       "      <td>0.241530</td>\n",
       "      <td>0.514724</td>\n",
       "      <td>0.562208</td>\n",
       "      <td>0.158963</td>\n",
       "      <td>0.073715</td>\n",
       "      <td>0.208463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.325261</td>\n",
       "      <td>0.805968</td>\n",
       "      <td>0.957033</td>\n",
       "      <td>0.331665</td>\n",
       "      <td>0.307923</td>\n",
       "      <td>0.355315</td>\n",
       "      <td>0.501899</td>\n",
       "      <td>0.558449</td>\n",
       "      <td>0.885169</td>\n",
       "      <td>0.182754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7  \\\n",
       "0  0.263995  0.764929  0.138424  0.935242  0.605867  0.518790  0.912225   \n",
       "1  0.546092  0.653975  0.065575  0.227772  0.845269  0.837066  0.272379   \n",
       "2  0.336714  0.538842  0.192801  0.553563  0.074515  0.332993  0.365792   \n",
       "3  0.092108  0.995017  0.014465  0.176371  0.241530  0.514724  0.562208   \n",
       "4  0.325261  0.805968  0.957033  0.331665  0.307923  0.355315  0.501899   \n",
       "\n",
       "       Col8      Col9     Col10  \n",
       "0  0.608234  0.723782  0.733591  \n",
       "1  0.331679  0.429297  0.367422  \n",
       "2  0.861309  0.899017  0.088600  \n",
       "3  0.158963  0.073715  0.208463  \n",
       "4  0.558449  0.885169  0.182754  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = get_config('X')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.anomaly import set_config\n",
    "set_config('seed', 999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_config('seed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. MLFlow UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mlflow ui"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End\n",
    "Thank you. For more information / tutorials on PyCaret, please visit https://www.pycaret.org"
   ]
  }
 ],
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